SIGNATURE WORK
CONFERENCE & EXHIBITION 2023

I write a paper which, presents a novel approach to stock price prediction using a combination of LSTM or Transformer models with GANs and named them LSTM-based GAN and Transformer-based GAN. The objective of this paper is to improve the accuracy and reliability of stock price forecasting by incorporating the benefits of GANs, which generate realistic data that can be used for training models. The results of the experiments demonstrate that the proposed method achieves higher prediction accuracy than traditional stock price prediction techniques. Additionally, the Transformer-based GAN outperforms the LSTM-based GAN, indicating that the former has the potential to be a more effective model for stock price prediction. In terms of future work, the model could be extended from a single stock forecast to multiple stock forecasts. This paper provides a promising avenue for improving the accuracy and reliability of stock price forecasting.